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new 40ba092a6e minor docs (#13281)
40ba092a6e is described below
commit 40ba092a6e39f3e6ccf1eb1428e37ca90cfc4a1d
Author: Jonathan Chen <[email protected]>
AuthorDate: Thu Nov 7 11:35:11 2024 -0500
minor docs (#13281)
---
.../aggregation_fuzzer/data_generator.rs | 30 +++++++++++-----------
1 file changed, 15 insertions(+), 15 deletions(-)
diff --git
a/datafusion/core/tests/fuzz_cases/aggregation_fuzzer/data_generator.rs
b/datafusion/core/tests/fuzz_cases/aggregation_fuzzer/data_generator.rs
index 88133a134e..f0973826b5 100644
--- a/datafusion/core/tests/fuzz_cases/aggregation_fuzzer/data_generator.rs
+++ b/datafusion/core/tests/fuzz_cases/aggregation_fuzzer/data_generator.rs
@@ -39,17 +39,17 @@ use test_utils::{
stagger_batch,
};
-/// Config for Data sets generator
+/// Config for Dataset generator
///
/// # Parameters
/// - `columns`, you just need to define `column name`s and `column data
type`s
-/// fot the test datasets, and then they will be randomly generated from
generator
-/// when you can `generate` function
+/// for the test datasets, and then they will be randomly generated from
the generator
+/// when you call `generate` function
///
-/// - `rows_num_range`, the rows num of the datasets will be randomly
generated
-/// among this range
+/// - `rows_num_range`, the number of rows in the datasets will be randomly
generated
+/// within this range
///
-/// - `sort_keys`, if `sort_keys` are defined, when you can `generate`, the
generator
+/// - `sort_keys`, if `sort_keys` are defined, when you call the `generate`
function, the generator
/// will generate one `base dataset` firstly. Then the `base dataset`
will be sorted
/// based on each `sort_key` respectively. And finally `len(sort_keys) +
1` datasets
/// will be returned
@@ -65,7 +65,7 @@ pub struct DatasetGeneratorConfig {
/// Additional optional sort keys
///
/// The generated datasets always include a non-sorted copy. For each
- /// element in `sort_keys_set`, an additional datasets is created that
+ /// element in `sort_keys_set`, an additional dataset is created that
/// is sorted by these values as well.
pub sort_keys_set: Vec<Vec<String>>,
}
@@ -93,15 +93,15 @@ impl DatasetGeneratorConfig {
/// Dataset generator
///
-/// It will generate one random [`Dataset`]s when `generate` function is
called.
+/// It will generate one random [`Dataset`] when `generate` function is called.
///
/// The generation logic in `generate`:
///
/// - Randomly generate a base record from `batch_generator` firstly.
/// And `columns`, `rows_num_range` in `config`(detail can see
`DataSetsGeneratorConfig`),
/// will be used in generation.
-///
-/// - Sort the batch according to `sort_keys` in `config` to generator
another
+///
+/// - Sort the batch according to `sort_keys` in `config` to generate another
/// `len(sort_keys)` sorted batches.
///
/// - Split each batch to multiple batches which each sub-batch in has the
randomly `rows num`,
@@ -600,8 +600,8 @@ mod test {
fn test_generated_datasets() {
// The test datasets generation config
// We expect that after calling `generate`
- // - Generate 2 datasets
- // - They have 2 column "a" and "b",
+ // - Generates two datasets
+ // - They have two columns, "a" and "b",
// "a"'s type is `Utf8`, and "b"'s type is `UInt32`
// - One of them is unsorted, another is sorted by column "b"
// - Their rows num should be same and between [16, 32]
@@ -636,7 +636,7 @@ mod test {
let batch = &datasets[1].batches[0];
check_fields(batch);
- // One batches should be sort by "b"
+ // One of the batches should be sorted by "b"
let sorted_batches = &datasets[1].batches;
let b_vals = sorted_batches.iter().flat_map(|batch| {
let uint_array = batch
@@ -653,10 +653,10 @@ mod test {
prev_b_val = b_val;
}
- // Two batches should be same after sorting
+ // Two batches should be the same after sorting
check_equality_of_batches(&datasets[0].batches,
&datasets[1].batches).unwrap();
- // Rows num should between [16, 32]
+ // The number of rows should be between [16, 32]
let rows_num0 = datasets[0]
.batches
.iter()
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